A collaborative paper between the OpenKIM team and several other organizations has been published in Applied Physics Letters. Titled "An information-matching approach to optimal experimental design and active learning", the paper demonstrates the use of the Fisher information matrix to select the most informative training data to most accurately predict a specific quantity of interest (QoI). The paper demonstrates several applications across disciplines, including power systems, underwater acoustics, and interatomic potentials (IPs). The IP examples demonstrate fitting Stillinger-Weber potentials for MoS2 and Si to reproduce QoIs such as equations of state, elastic constants, and phonons using a minimal training dataset selected using the proposed method. The IP fitting workflow is enabled by the KLIFF fitting framework and the KIM API.